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Record W2322137658 · doi:10.1149/1.3207662

Controlled Synthesis of Carbon Nanotubes by Various CVD and PECVD Methods

2009· article· en· W2322137658 on OpenAlexaff
Mihnea Ioan Ionescu, H. Liu, Yu Lin Zhong, Youmin Zhang, R. Li, Xueliang Sun, J.-B. Kpetsu, Claude H. Côt́e, P. Jedrzejowsk, A. Sarkissian, Philippe Mérel, P. Laou, Suzanne Paradis, S. Désilets

Bibliographic record

VenueECS Transactions · 2009
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsPlasmionique (Canada)Defence Research and Development CanadaWestern University
Fundersnot available
KeywordsCarbon nanotubeMaterials sciencePlasma-enhanced chemical vapor depositionNanotechnologyRaman spectroscopyTransmission electron microscopyScanning electron microscopeChemical vapor depositionChemical engineeringComposite materialOptics

Abstract

fetched live from OpenAlex

In this paper, we review and present the results of carbon nanotubes syntheses using variety of CVD and PECVD techniques that are available in our laboratories. The techniques included Joule-heating CVD, floating catalyst CVD, conventional thermal CVD, aerosol-assisted CVD, spray pyrolysis CVD, radio frequency plasma enhanced CVD (PECVD) and microwave plasma enhanced CVD. Influences of synthesis methods on the CNTs growth have been compared and discussed. The synthesized CNTs have been characterized by various techniques, including Raman Spectroscopy, scanning electron microscopy (SEM) and transmission electron microscopy (TEM). These studies are expected to pave the way to develop strategies for controlled synthesis of CNTs tailored for specific applications, including sensors and nanodevices.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.271
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2009
Admission routes1
Has abstractyes

Explore more

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